The feed ranking seems to be based on two concepts – Importance of a post, Relevance to the person.

Importance of a post is measured using the reaction it gets in the form of likes, comments and shares. Given 4 people like a post with 10 impressions, it is expected to have a lesser impact that a post that has 8 likes in 10 impressions. We can expect the comments and shares to further boost the score, although multiple comments from the same person doesn’t add much. Also these signals decay with time. A like yesterday you got for your post yesterday doesn’t probably count much today and so the post goes down in the ranking.
ImportanceScore = (∑_i C_i * e^{-k(t_now-t_i)}) / NumImpressions
where k is some decaying constant which is multiplied to how long back the post was shared.  C_is are different constants for likes/comments/shares. NumImpressions is the number of times the post has been shown on screen.

Relevance to a person is important in deciding the probability a given person might like your post. I have observed that I see more posts from people who have interacted with me in the past. People whose posts I like/comment on see my posts higher up in their list. The relevance/friendship score can be determined by going through all the interactions between person X and person Y and summing them up with decaying time and weighted based on the type of interactions. This ensures that the recent interactions are more important.

Relevance/Friendship (X, Y) = ∑_i C_i * e^{-k(t_now-t_i)}
where k is again a constant and is multiplied to the ‘how long back the interaction happened’. C_is are different constants of interaction (chat/message/comment/like/share/tag/share image/appear in same post/appear in same image)

In addition ‘relevance’ can also take into account the number of your friends who liked my post to predict the probability of you liking it.

Other factors that could be considered.
Social Status of the people who like/share/comment on your posts:-
People who like everything are expected to contribute less to the importance scores as opposed to people who like selectively. People with more number of friends interacting with him (note the direction of interaction), who has a higher social rank is expected to contribute more importance to your post than a guy with just one friend. The calculation of Social Rank is far more complex and I will omit the formulae and the confusion in this blog.

I have also ignored concept recognition as of now, the feature that allows FB to find out what you are talking about. For example “Indian National Cricket Team” or “Kolaveri”. Essentially I have considered only posts that talk about things that other people are not talking about. To adapt the model to include this signal, we would have to cluster all these posts together like Facebook does and use the scores of other posts in the cluster to score a new post.

So, how can we beat the system? Here is where I present some stuff that shameless people can try

• If the first few people who see your post like it, it gets a high rank immediately and lots of people end up seeing it improving the chances of getting more ‘likes’.
• If you get the popular social centres to get to like your posts, your posts are likely to get a higher scores.
• Interacting (chatting with, commenting…) with more people on Facebook from different friend clusters (school, high school, college, university, work, home, relatives) could help.
• Sharing obviously funny/likable things that are bound to get a lot of likes once in a while to improve friendship_score. (“Go Sachin Tendulkar”/ Kitten pictures / stolen jokes etc)

Other things that could likely help

• Replacing the words Google-Plus in your posts with G_o_o-g_l_e + may be? And Sonia Gandhi with 50|\|i@ G@|\|_|) |-|i.
• Using Images with text in them rather than using plain text as it increases that chances of being noticed.

I have just given my opinions in this post with a language which is as less geeky/mathematical as possible, you can share your opinions/other things you have observed about the algorithm in comments. Please also share if you found this interesting or funny. Nothing can please the author of an ad-free blog more than likes and comments.

Wikipedia

Requirements:

• LAMP (Linux, Apache, MySQL, PHP)
• Around 30 GB of space in primary partition 30 GB of space for storage. In my case the root partition
• 7 GB of free Internet download
• 3 days of free time

Wikipedia dumps can be downloaded from the Wikipedia site in XML format compressed in .7zip. This is around 6 GBs when compressed and expands to around 25GB of XML pages. It doesn’t include any images. This page shows how one can extract text articles from articles and construct corpuses from the same. Apart from this, a static HTML dump can also be downloaded from Wikipedia page (wikipedia-en-html.tar.7z) and this version has images in it. The compressed version is at 15 GB and it expands to over 200 GB because of all the images.

The Static HTML dump can simply be extracted to get all the HTML files and the required HTML file can be opened to view the required content. In case you download the XML dump, there is more – you have to extract the articles and create your customized offline Wikipedia.with the following steps.

1. Download the latest mediawiki and install it on your Linux/Windows machine using LAMP/WAMP/XAMPP. Mediawiki is the software that renders Wikipedia articles using the data stored in MySQL.
2. Mediawiki needs a few extensions which have been installed in Wikipedia.Once we have mediawiki installed say /var/www/wiki/, download each of them and install by extracting these extensions in the /var/www/wiki/extensions directory.
   The following extensions have to be installed – CategoryTree, CharInsert, Cite, ImageMap, InputBox, ParserFunctions (very important), Poem, randomSelection, SyntaxHighlight-GeSHi, timeline, wikihero which can all be found in the Mediawiki extensions download page by following the instructions. In addition you can install any template to make your wiki look like whatever you want. Now your own Wiki is ready for you to use, you can add your own articles but what we want now is to copy the original Wikipedia articles to our Wiki.
3. It is easy to import all the data once and then construct an index for the data in MySQL than to update the index each time an article is added. Open MySQL and your database, the tables that are used in the import are text, page and revs. You can delete all the indexes on that page and create it again in the 5th step to speed up the process.
4. Now that we have our XML database, we need to import it into the MySQL database. You can find the instructions here. In short, a summary of the instructions found on that page, the ONLY WAY you can get Wikipedia really fast on your computer is to use mwdumpertool to import into the database. The inbuilt tool in mediawiki won’t work fast and may run for several days. The following command can be used to import the dump into the database within an hour.

   java -jar mwdumper.jar --format=sql:1.5 <dump_name> | mysql -u <username> -p <databasename>

5.  Recreate the indexes on the tables ‘page’, ‘revs’ and ‘text’ and you are done.

You can comment if you want to try the same or if you run into any problems while trying.

Stack-overflow

Requirements

• LAMP (Linux, Apache, MySQL, PHP)
• Around 15 GB of space in the primary partition and 15 GB of storage. In my case the root partition
• 4 GB of free Internet download

media10.simplex.tv/content/xtendx/stu/stackoverflow has several stackoverflow zip files available for direct download. Alternatively, stack-overflow dumps can be downloaded using a torrent. A torrent download can be converted into an FTP download using http://www.torrific.com/home/. Once you have the dumps you can unpack them to get huge XML files for several stack sites. Stack-Overflow is one of the stack sites, the 7zip file is broken into 4 parts and have to be combined using a command (cat a.xml b.xml c.xml d.xml > full.xml) Once combined and extracted, we can see 6 xml files for each site (badges, comments, postHistory, posts, users, votes, ) Among these, comments, posts and votes may seem useful for offline usage of the forum. A main post may consist of several reply posts and each such post may have follow-up comments. Votes are used to rate an answer and they can be used as signals while you browse through questions. Follow the following steps to import the data into the database and use the UI to browse posts offline.

• Download Stack sites
• Create a database StackOverflow with the schema using the description here. (comments, posts and votes tables are enough)
• Use the code to import the data to the database. (Suitably modify the variables serveraddress, port, username, password, databasename, rowspercommit, filePath and site in the code)
• Run the code on Stack Mathematics to import the mathematics site. For bigger sites, it may take much more time and a lot of optimizations are needed along with a lot of disk space in the primary partition where the MySQL stores its databases.
• Use the UI php files to view a post given the post number along with the comments and replies.
• TODO: Additionally we can add a search engine that searches the table ‘posts’ for queries and returns post numbers which match the same.